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Sensors, Volume 18, Issue 1 (January 2018)

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Cover Story (view full-size image) The joint use of heterogeneous sensors and artificial intelligence techniques for the simultaneous [...] Read more.
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Open AccessArticle EKF–GPR-Based Fingerprint Renovation for Subset-Based Indoor Localization with Adjusted Cosine Similarity
Sensors 2018, 18(1), 318; https://doi.org/10.3390/s18010318
Received: 27 December 2017 / Revised: 18 January 2018 / Accepted: 21 January 2018 / Published: 22 January 2018
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Abstract
Received Signal Strength Indicator (RSSI) localization using fingerprint has become a prevailing approach for indoor localization. However, the fingerprint-collecting work is repetitive and time-consuming. After the original fingerprint radio map is built, it is laborious to upgrade the radio map. In this paper,
[...] Read more.
Received Signal Strength Indicator (RSSI) localization using fingerprint has become a prevailing approach for indoor localization. However, the fingerprint-collecting work is repetitive and time-consuming. After the original fingerprint radio map is built, it is laborious to upgrade the radio map. In this paper, we describe a Fingerprint Renovation System (FRS) based on crowdsourcing, which avoids the use of manual labour to obtain the up-to-date fingerprint status. Extended Kalman Filter (EKF) and Gaussian Process Regression (GPR) in FRS are combined to calculate the current state based on the original fingerprinting radio map. In this system, a method of subset acquisition also makes an immediate impression to reduce the huge computation caused by too many reference points (RPs). Meanwhile, adjusted cosine similarity (ACS) is employed in the online phase to solve the issue of outliers produced by cosine similarity. Both experiments and analytical simulation in a real Wireless Fidelity (Wi-Fi) environment indicate the usefulness of our system to significant performance improvements. The results show that FRS improves the accuracy by 19.6% in the surveyed area compared to the radio map un-renovated. Moreover, the proposed subset algorithm can bring less computation. Full article
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Open AccessCommunication Voltammetric Response of Alizarin Red S-Confined Film-Coated Electrodes to Diol and Polyol Compounds: Use of Phenylboronic Acid-Modified Poly(ethyleneimine) as Film Component
Sensors 2018, 18(1), 317; https://doi.org/10.3390/s18010317
Received: 5 January 2018 / Revised: 19 January 2018 / Accepted: 19 January 2018 / Published: 22 January 2018
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Abstract
Alizarin red S (ARS) was confined in layer-by-layer (LbL) films composed of phenylboronic acid-modified poly(ethyleneimine) (PBA-PEI) and carboxymethylcellulose (CMC) to study the voltammetric response to diol and polyol compounds. The LbL film-coated gold (Au) electrode and quartz slide were immersed in an ARS
[...] Read more.
Alizarin red S (ARS) was confined in layer-by-layer (LbL) films composed of phenylboronic acid-modified poly(ethyleneimine) (PBA-PEI) and carboxymethylcellulose (CMC) to study the voltammetric response to diol and polyol compounds. The LbL film-coated gold (Au) electrode and quartz slide were immersed in an ARS solution to uptake ARS into the film. UV-visible absorption spectra of ARS-confined LbL film suggested that ARS formed boronate ester (ARS-PBS) in the film. The cyclic voltammetry of the ARS-confined LbL film-coated electrodes exhibited oxidation peaks at −0.50 and −0.62 V, which were ascribed to the oxidation reactions of ARS-PBS and free ARS, respectively, in the LbL film. The peak current at −0.62 V increased upon the addition of diol or polyol compounds such as L-dopa, glucose, and sorbitol into the solution, depending on the concentration, whereas the peak current at −0.50 V decreased. The results suggest a possible use of ARS-confined PBA-PEI/CMC LbL film-coated Au electrodes for the construction of voltammetric sensors for diol and polyol compounds. Full article
(This article belongs to the Section Biosensors)
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Open AccessArticle Mapping of Rice Varieties and Sowing Date Using X-Band SAR Data
Sensors 2018, 18(1), 316; https://doi.org/10.3390/s18010316
Received: 6 November 2017 / Revised: 17 January 2018 / Accepted: 18 January 2018 / Published: 22 January 2018
Cited by 3 | Viewed by 1283 | PDF Full-text (9989 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Rice is a major staple food for nearly half of the world’s population and has a considerable contribution to the global agricultural economy. While spaceborne Synthetic Aperture Radar (SAR) data have proved to have great potential to provide rice cultivation area, few studies
[...] Read more.
Rice is a major staple food for nearly half of the world’s population and has a considerable contribution to the global agricultural economy. While spaceborne Synthetic Aperture Radar (SAR) data have proved to have great potential to provide rice cultivation area, few studies have been performed to provide practical information that meets the user requirements. In rice growing regions where the inter-field crop calendar is not uniform such as in the Mekong Delta in Vietnam, knowledge of the start of season on a field basis, along with the planted rice varieties, is very important for correct field management (timing of irrigation, fertilization, chemical treatment, harvest), and for market assessment of the rice production. The objective of this study is to develop methods using SAR data to retrieve in addition to the rice grown area, the sowing date, and the distinction between long and short cycle varieties. This study makes use of X-band SAR data from COSMO-SkyMed acquired from 19 August to 23 November 2013 covering the Chau Thanh and Thoai Son districts in An Giang province, Viet Nam, characterized by a complex cropping pattern. The SAR data have been analyzed as a function of rice parameters, and the temporal and polarization behaviors of the radar backscatter of different rice varieties have been interpreted physically. New backscatter indicators for the detection of rice paddy area, the estimation of the sowing date, and the mapping of the short cycle and long cycle rice varieties have been developed and assessed. Good accuracy has been found with 92% in rice grown area, 96% on rice long or short cycle, and a root mean square error of 4.3 days in sowing date. The results have been discussed regarding the generality of the methods with respect to the rice cultural practices and the SAR data characteristics. Full article
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Open AccessArticle An Inverse Neural Controller Based on the Applicability Domain of RBF Network Models
Sensors 2018, 18(1), 315; https://doi.org/10.3390/s18010315
Received: 7 November 2017 / Revised: 6 January 2018 / Accepted: 18 January 2018 / Published: 22 January 2018
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Abstract
This paper presents a novel methodology of generic nature for controlling nonlinear systems, using inverse radial basis function neural network models, which may combine diverse data originating from various sources. The algorithm starts by applying the particle swarm optimization-based non-symmetric variant of the
[...] Read more.
This paper presents a novel methodology of generic nature for controlling nonlinear systems, using inverse radial basis function neural network models, which may combine diverse data originating from various sources. The algorithm starts by applying the particle swarm optimization-based non-symmetric variant of the fuzzy means (PSO-NSFM) algorithm so that an approximation of the inverse system dynamics is obtained. PSO-NSFM offers models of high accuracy combined with small network structures. Next, the applicability domain concept is suitably tailored and embedded into the proposed control structure in order to ensure that extrapolation is avoided in the controller predictions. Finally, an error correction term, estimating the error produced by the unmodeled dynamics and/or unmeasured external disturbances, is included to the control scheme to increase robustness. The resulting controller guarantees bounded input-bounded state (BIBS) stability for the closed loop system when the open loop system is BIBS stable. The proposed methodology is evaluated on two different control problems, namely, the control of an experimental armature-controlled direct current (DC) motor and the stabilization of a highly nonlinear simulated inverted pendulum. For each one of these problems, appropriate case studies are tested, in which a conventional neural controller employing inverse models and a PID controller are also applied. The results reveal the ability of the proposed control scheme to handle and manipulate diverse data through a data fusion approach and illustrate the superiority of the method in terms of faster and less oscillatory responses. Full article
(This article belongs to the Special Issue Soft Sensors and Intelligent Algorithms for Data Fusion)
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Open AccessFeature PaperArticle A Real-Time Ultraviolet Radiation Imaging System Using an Organic Photoconductive Image Sensor
Sensors 2018, 18(1), 314; https://doi.org/10.3390/s18010314
Received: 31 October 2017 / Revised: 12 January 2018 / Accepted: 18 January 2018 / Published: 22 January 2018
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Abstract
We have developed a real time ultraviolet (UV) imaging system that can visualize both invisible UV light and a visible (VIS) background scene in an outdoor environment. As a UV/VIS image sensor, an organic photoconductive film (OPF) imager is employed. The OPF has
[...] Read more.
We have developed a real time ultraviolet (UV) imaging system that can visualize both invisible UV light and a visible (VIS) background scene in an outdoor environment. As a UV/VIS image sensor, an organic photoconductive film (OPF) imager is employed. The OPF has an intrinsically higher sensitivity in the UV wavelength region than those of conventional consumer Complementary Metal Oxide Semiconductor (CMOS) image sensors (CIS) or Charge Coupled Devices (CCD). As particular examples, imaging of hydrogen flame and of corona discharge is demonstrated. UV images overlapped on background scenes are simply made by on-board background subtraction. The system is capable of imaging weaker UV signals by four orders of magnitude than that of VIS background. It is applicable not only to future hydrogen supply stations but also to other UV/VIS monitor systems requiring UV sensitivity under strong visible radiation environment such as power supply substations. Full article
(This article belongs to the Special Issue Special Issue on the 2017 International Image Sensor Workshop (IISW))
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Open AccessArticle A Survey of Data Semantization in Internet of Things
Sensors 2018, 18(1), 313; https://doi.org/10.3390/s18010313
Received: 30 November 2017 / Revised: 16 January 2018 / Accepted: 18 January 2018 / Published: 22 January 2018
Cited by 2 | Viewed by 1419 | PDF Full-text (2049 KB) | HTML Full-text | XML Full-text
Abstract
With the development of Internet of Things (IoT), more and more sensors, actuators and mobile devices have been deployed into our daily lives. The result is that tremendous data are produced and it is urgent to dig out hidden information behind these volumous
[...] Read more.
With the development of Internet of Things (IoT), more and more sensors, actuators and mobile devices have been deployed into our daily lives. The result is that tremendous data are produced and it is urgent to dig out hidden information behind these volumous data. However, IoT data generated by multi-modal sensors or devices show great differences in formats, domains and types, which poses challenges for machines to process and understand. Therefore, adding semantics to Internet of Things becomes an overwhelming tendency. This paper provides a systematic review of data semantization in IoT, including its backgrounds, processing flows, prevalent techniques, applications, existing challenges and open issues. It surveys development status of adding semantics to IoT data, mainly referring to sensor data and points out current issues and challenges that are worth further study. Full article
(This article belongs to the Special Issue Sensing, Data Analysis and Platforms for Ubiquitous Intelligence)
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Open AccessArticle Dual Channel S-Band Frequency Modulated Continuous Wave Through-Wall Radar Imaging
Sensors 2018, 18(1), 311; https://doi.org/10.3390/s18010311
Received: 6 December 2017 / Revised: 12 January 2018 / Accepted: 16 January 2018 / Published: 22 January 2018
Cited by 1 | Viewed by 1107 | PDF Full-text (7231 KB) | HTML Full-text | XML Full-text
Abstract
This article deals with the development of a dual channel S-Band frequency-modulated continuous wave (FMCW) system for a through-the-wall imaging (TWRI) system. Most existing TWRI systems using FMCW were developed for synthetic aperture radar (SAR) which has many drawbacks such as the need
[...] Read more.
This article deals with the development of a dual channel S-Band frequency-modulated continuous wave (FMCW) system for a through-the-wall imaging (TWRI) system. Most existing TWRI systems using FMCW were developed for synthetic aperture radar (SAR) which has many drawbacks such as the need for several antenna elements and movement of the system. Our implemented TWRI system comprises a transmitting antenna and two receiving antennas, resulting in a significant reduction of the number of antenna elements. Moreover, a proposed algorithm for range-angle-Doppler 3D estimation based on a 3D shift invariant structure is utilized in our implemented dual channel S-band FMCW TWRI system. Indoor and outdoor experiments were conducted to image the scene beyond a wall for water targets and person targets, respectively. The experimental results demonstrate that high-quality imaging can be achieved under both experimental scenarios. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle Online Removal of Baseline Shift with a Polynomial Function for Hemodynamic Monitoring Using Near-Infrared Spectroscopy
Sensors 2018, 18(1), 312; https://doi.org/10.3390/s18010312
Received: 29 November 2017 / Revised: 13 January 2018 / Accepted: 16 January 2018 / Published: 21 January 2018
Cited by 5 | Viewed by 1040 | PDF Full-text (1932 KB) | HTML Full-text | XML Full-text
Abstract
Near-infrared spectroscopy (NIRS) has become widely accepted as a valuable tool for noninvasively monitoring hemodynamics for clinical and diagnostic purposes. Baseline shift has attracted great attention in the field, but there has been little quantitative study on baseline removal. Here, we aimed to
[...] Read more.
Near-infrared spectroscopy (NIRS) has become widely accepted as a valuable tool for noninvasively monitoring hemodynamics for clinical and diagnostic purposes. Baseline shift has attracted great attention in the field, but there has been little quantitative study on baseline removal. Here, we aimed to study the baseline characteristics of an in-house-built portable medical NIRS device over a long time (>3.5 h). We found that the measured baselines all formed perfect polynomial functions on phantom tests mimicking human bodies, which were identified by recent NIRS studies. More importantly, our study shows that the fourth-order polynomial function acted to distinguish performance with stable and low-computation-burden fitting calibration (R-square >0.99 for all probes) among second- to sixth-order polynomials, evaluated by the parameters R-square, sum of squares due to error, and residual. This study provides a straightforward, efficient, and quantitatively evaluated solution for online baseline removal for hemodynamic monitoring using NIRS devices. Full article
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Open AccessArticle Textile Concentric Ring Electrodes for ECG Recording Based on Screen-Printing Technology
Sensors 2018, 18(1), 300; https://doi.org/10.3390/s18010300
Received: 29 December 2017 / Revised: 16 January 2018 / Accepted: 17 January 2018 / Published: 21 January 2018
Cited by 1 | Viewed by 1543 | PDF Full-text (3428 KB) | HTML Full-text | XML Full-text
Abstract
Among many of the electrode designs used in electrocardiography (ECG), concentric ring electrodes (CREs) are one of the most promising due to their enhanced spatial resolution. Their development has undergone a great push due to their use in recent years; however, they are
[...] Read more.
Among many of the electrode designs used in electrocardiography (ECG), concentric ring electrodes (CREs) are one of the most promising due to their enhanced spatial resolution. Their development has undergone a great push due to their use in recent years; however, they are not yet widely used in clinical practice. CRE implementation in textiles will lead to a low cost, flexible, comfortable, and robust electrode capable of detecting high spatial resolution ECG signals. A textile CRE set has been designed and developed using screen-printing technology. This is a mature technology in the textile industry and, therefore, does not require heavy investments. Inks employed as conductive elements have been silver and a conducting polymer (poly (3,4-ethylenedioxythiophene) polystyrene sulfonate; PEDOT:PSS). Conducting polymers have biocompatibility advantages, they can be used with flexible substrates, and they are available for several printing technologies. CREs implemented with both inks have been compared by analyzing their electric features and their performance in detecting ECG signals. The results reveal that silver CREs present a higher average thickness and slightly lower skin-electrode impedance than PEDOT:PSS CREs. As for ECG recordings with subjects at rest, both CREs allowed the uptake of bipolar concentric ECG signals (BC-ECG) with signal-to-noise ratios similar to that of conventional ECG recordings. Regarding the saturation and alterations of ECGs captured with textile CREs caused by intentional subject movements, silver CREs presented a more stable response (fewer saturations and alterations) than those of PEDOT:PSS. Moreover, BC-ECG signals provided higher spatial resolution compared to conventional ECG. This improved spatial resolution was manifested in the identification of P1 and P2 waves of atrial activity in most of the BC-ECG signals. It can be concluded that textile silver CREs are more suitable than those of PEDOT:PSS for obtaining BC-ECG records. These developed textile electrodes bring the use of CREs closer to the clinical environment. Full article
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Open AccessArticle A Circular Microstrip Antenna Sensor for Direction Sensitive Strain Evaluation
Sensors 2018, 18(1), 310; https://doi.org/10.3390/s18010310
Received: 6 December 2017 / Revised: 9 January 2018 / Accepted: 17 January 2018 / Published: 20 January 2018
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Abstract
In this paper, a circular microstrip antenna for stress evaluation is studied. This kind of microstrip sensor can be utilized in structural health monitoring systems. Reflection coefficient S11 is measured to determine deformation/strain value. The proposed sensor is adhesively connected to the
[...] Read more.
In this paper, a circular microstrip antenna for stress evaluation is studied. This kind of microstrip sensor can be utilized in structural health monitoring systems. Reflection coefficient S11 is measured to determine deformation/strain value. The proposed sensor is adhesively connected to the studied sample. Applied strain causes a change in patch geometry and influences current distribution both in patch and ground plane. Changing the current flow in patch influences the value of resonant frequency. In this paper, two different resonant frequencies were analysed because in each case, different current distributions in patch were obtained. The sensor was designed for operating frequency of 2.5 GHz (at fundamental mode), which results in a diameter less than 55 mm. Obtained sensitivity was up to 1 MHz/100 MPa, resolution depends on utilized vector network analyser. Moreover, the directional characteristics for both resonant frequencies were defined, studied using numerical model and verified by measurements. Thus far, microstrip antennas have been used in deformation measurement only if the direction of external force was well known. Obtained directional characteristics of the sensor allow the determination of direction and value of stress by one sensor. This method of measurement can be an alternative to the rosette strain gauge. Full article
(This article belongs to the Special Issue Small Devices and the High-Tech Society)
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Open AccessArticle Overhead Transmission Line Sag Estimation Using a Simple Optomechanical System with Chirped Fiber Bragg Gratings. Part 1: Preliminary Measurements
Sensors 2018, 18(1), 309; https://doi.org/10.3390/s18010309
Received: 27 November 2017 / Revised: 14 January 2018 / Accepted: 18 January 2018 / Published: 20 January 2018
Cited by 4 | Viewed by 1388 | PDF Full-text (4686 KB) | HTML Full-text | XML Full-text
Abstract
A method of measuring the power line wire sag using optical sensors that are insensitive to high electromagnetic fields was proposed. The advantage of this technique is that it is a non-invasive measurement of power line wire elongation using a unique optomechanical system.
[...] Read more.
A method of measuring the power line wire sag using optical sensors that are insensitive to high electromagnetic fields was proposed. The advantage of this technique is that it is a non-invasive measurement of power line wire elongation using a unique optomechanical system. The proposed method replaces the sag of the power line wire with an extension of the control sample and then an expansion of the attached chirped fiber Bragg grating. This paper presents the results of the first measurements made on real aluminum-conducting steel-reinforced wire, frequently used for power line construction. It has been shown that the proper selection of the CFBG (chirped fiber Bragg grating) transducer and the appropriate choice of optical parameters of such a sensor will allow for high sensitivity of the line wire elongation and sag while reducing the sensitivity to the temperature. It has been shown that with a simple optomechanical system, a non-invasive measurement of the power line wire sag that is insensitive to temperature changes and the influence of high electromagnetic fields can be achieved. Full article
(This article belongs to the Special Issue Optical Fiber Sensors 2017)
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Open AccessCommunication Development of a Label-Free Immunosensor for Clusterin Detection as an Alzheimer’s Biomarker
Sensors 2018, 18(1), 308; https://doi.org/10.3390/s18010308
Received: 1 November 2017 / Revised: 10 January 2018 / Accepted: 18 January 2018 / Published: 20 January 2018
Cited by 2 | Viewed by 1637 | PDF Full-text (2947 KB) | HTML Full-text | XML Full-text
Abstract
Clusterin (CLU) has been associated with the clinical progression of Alzheimer’s disease (AD) and described as a potential AD biomarker in blood plasma. Due to the enormous attention given to cerebrospinal fluid (CSF) biomarkers for the past couple of decades, recently found blood-based
[...] Read more.
Clusterin (CLU) has been associated with the clinical progression of Alzheimer’s disease (AD) and described as a potential AD biomarker in blood plasma. Due to the enormous attention given to cerebrospinal fluid (CSF) biomarkers for the past couple of decades, recently found blood-based AD biomarkers like CLU have not yet been reported for biosensors. Herein, we report the electrochemical detection of CLU for the first time using a screen-printed carbon electrode (SPCE) modified with 1-pyrenebutyric acid N-hydroxysuccinimide ester (Pyr-NHS) and decorated with specific anti-CLU antibody fragments. This bifunctional linker molecule contains succinylimide ester to bind protein at one end while its pyrene moiety attaches to the carbon surface by means of π-π stacking. Cyclic voltammetric and square wave voltammetric studies showed the limit of detection down to 1 pg/mL and a linear concentration range of 1–100 pg/mL with good sensitivity. Detection of CLU in spiked human plasma was demonstrated with satisfactory recovery percentages to that of the calibration data. The proposed method facilitates the cost-effective and viable production of label-free point-of-care devices for the clinical diagnosis of AD. Full article
(This article belongs to the Special Issue Label-Free Biosensors)
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Open AccessArticle An Antibody-Immobilized Silica Inverse Opal Nanostructure for Label-Free Optical Biosensors
Sensors 2018, 18(1), 307; https://doi.org/10.3390/s18010307
Received: 8 December 2017 / Revised: 16 January 2018 / Accepted: 16 January 2018 / Published: 20 January 2018
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Abstract
Three-dimensional SiO2-based inverse opal (SiO2-IO) nanostructures were prepared for use as biosensors. SiO2-IO was fabricated by vertical deposition and calcination processes. Antibodies were immobilized on the surface of SiO2-IO using 3-aminopropyl trimethoxysilane (APTMS), a succinimidyl-[(N-maleimidopropionamido)-tetraethyleneglycol]
[...] Read more.
Three-dimensional SiO2-based inverse opal (SiO2-IO) nanostructures were prepared for use as biosensors. SiO2-IO was fabricated by vertical deposition and calcination processes. Antibodies were immobilized on the surface of SiO2-IO using 3-aminopropyl trimethoxysilane (APTMS), a succinimidyl-[(N-maleimidopropionamido)-tetraethyleneglycol] ester (NHS-PEG4-maleimide) cross-linker, and protein G. The highly accessible surface and porous structure of SiO2-IO were beneficial for capturing influenza viruses on the antibody-immobilized surfaces. Moreover, as the binding leads to the redshift of the reflectance peak, the influenza virus could be detected by simply monitoring the change in the reflectance spectrum without labeling. SiO2-IO showed high sensitivity in the range of 103–105 plaque forming unit (PFU) and high specificity to the influenza A (H1N1) virus. Due to its structural and optical properties, SiO2-IO is a promising material for the detection of the influenza virus. Our study provides a generalized sensing platform for biohazards as various sensing strategies can be employed through the surface functionalization of three-dimensional nanostructures. Full article
(This article belongs to the Special Issue Nanostructured Hybrid Materials Based Opto-Electronics Sensors)
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Open AccessArticle L-Tree: A Local-Area-Learning-Based Tree Induction Algorithm for Image Classification
Sensors 2018, 18(1), 306; https://doi.org/10.3390/s18010306
Received: 13 October 2017 / Revised: 5 January 2018 / Accepted: 18 January 2018 / Published: 20 January 2018
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Abstract
The decision tree is one of the most effective tools for deriving meaningful outcomes from image data acquired from the visual sensors. Owing to its reliability, superior generalization abilities, and easy implementation, the tree model has been widely used in various applications. However,
[...] Read more.
The decision tree is one of the most effective tools for deriving meaningful outcomes from image data acquired from the visual sensors. Owing to its reliability, superior generalization abilities, and easy implementation, the tree model has been widely used in various applications. However, in image classification problems, conventional tree methods use only a few sparse attributes as the splitting criterion. Consequently, they suffer from several drawbacks in terms of performance and environmental sensitivity. To overcome these limitations, this paper introduces a new tree induction algorithm that classifies images on the basis of local area learning. To train our predictive model, we extract a random local area within the image and use it as a feature for classification. In addition, the self-organizing map, which is a clustering technique, is used for node learning. We also adopt a random sampled optimization technique to search for the optimal node. Finally, each trained node stores the weights that represent the training data and class probabilities. Thus, a recursively trained tree classifies the data hierarchically based on the local similarity at each node. The proposed tree is a type of predictive model that offers benefits in terms of image’s semantic energy conservation compared with conventional tree methods. Consequently, it exhibits improved performance under various conditions, such as noise and illumination changes. Moreover, the proposed algorithm can improve the generalization ability owing to its randomness. In addition, it can be easily applied to ensemble techniques. To evaluate the performance of the proposed algorithm, we perform quantitative and qualitative comparisons with various tree-based methods using four image datasets. The results show that our algorithm not only involves a lower classification error than the conventional methods but also exhibits stable performance even under unfavorable conditions such as noise and illumination changes. Full article
(This article belongs to the Special Issue Smart Decision-Making)
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Open AccessArticle A 750 K Photocharge Linear Full Well in a 3.2 μm HDR Pixel with Complementary Carrier Collection
Sensors 2018, 18(1), 305; https://doi.org/10.3390/s18010305
Received: 1 November 2017 / Revised: 12 January 2018 / Accepted: 14 January 2018 / Published: 20 January 2018
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Abstract
Mainly driven by automotive applications, there is an increasing interest in image sensors combining a high dynamic range (HDR) and immunity to the flicker issue. The native HDR pixel concept based on a parallel electron and hole collection for, respectively, a low signal
[...] Read more.
Mainly driven by automotive applications, there is an increasing interest in image sensors combining a high dynamic range (HDR) and immunity to the flicker issue. The native HDR pixel concept based on a parallel electron and hole collection for, respectively, a low signal level and a high signal level is particularly well-suited for this performance challenge. The theoretical performance of this pixel is modeled and compared to alternative HDR pixel architectures. This concept is proven with the fabrication of a 3.2 μm pixel in a back-side illuminated (BSI) process including capacitive deep trench isolation (CDTI). The electron-based image uses a standard 4T architecture with a pinned diode and provides state-of-the-art low-light performance, which is not altered by the pixel modifications introduced for the hole collection. The hole-based image reaches 750 kh+ linear storage capability thanks to a 73 fF CDTI capacitor. Both images are taken from the same integration window, so the HDR reconstruction is not only immune to the flicker issue but also to motion artifacts. Full article
(This article belongs to the Special Issue Special Issue on the 2017 International Image Sensor Workshop (IISW))
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Open AccessArticle An IMU-Aided Body-Shadowing Error Compensation Method for Indoor Bluetooth Positioning
Sensors 2018, 18(1), 304; https://doi.org/10.3390/s18010304
Received: 31 December 2017 / Revised: 14 January 2018 / Accepted: 17 January 2018 / Published: 20 January 2018
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Abstract
Research on indoor positioning technologies has recently become a hotspot because of the huge social and economic potential of indoor location-based services (ILBS). Wireless positioning signals have a considerable attenuation in received signal strength (RSS) when transmitting through human bodies, which would cause
[...] Read more.
Research on indoor positioning technologies has recently become a hotspot because of the huge social and economic potential of indoor location-based services (ILBS). Wireless positioning signals have a considerable attenuation in received signal strength (RSS) when transmitting through human bodies, which would cause significant ranging and positioning errors in RSS-based systems. This paper mainly focuses on the body-shadowing impairment of RSS-based ranging and positioning, and derives a mathematical expression of the relation between the body-shadowing effect and the positioning error. In addition, an inertial measurement unit-aided (IMU-aided) body-shadowing detection strategy is designed, and an error compensation model is established to mitigate the effect of body-shadowing. A Bluetooth positioning algorithm with body-shadowing error compensation (BP-BEC) is then proposed to improve both the positioning accuracy and the robustness in indoor body-shadowing environments. Experiments are conducted in two indoor test beds, and the performance of both the BP-BEC algorithm and the algorithms without body-shadowing error compensation (named no-BEC) is evaluated. The results show that the BP-BEC outperforms the no-BEC by about 60.1% and 73.6% in terms of positioning accuracy and robustness, respectively. Moreover, the execution time of the BP-BEC algorithm is also evaluated, and results show that the convergence speed of the proposed algorithm has an insignificant effect on real-time localization. Full article
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Open AccessArticle Three-Dimensional Terahertz Coded-Aperture Imaging Based on Single Input Multiple Output Technology
Sensors 2018, 18(1), 303; https://doi.org/10.3390/s18010303
Received: 15 December 2017 / Revised: 17 January 2018 / Accepted: 18 January 2018 / Published: 19 January 2018
Cited by 4 | Viewed by 1193 | PDF Full-text (8266 KB) | HTML Full-text | XML Full-text
Abstract
As a promising radar imaging technique, terahertz coded-aperture imaging (TCAI) can achieve high-resolution, forward-looking, and staring imaging by producing spatiotemporal independent signals with coded apertures. In this paper, we propose a three-dimensional (3D) TCAI architecture based on single input multiple output (SIMO) technology,
[...] Read more.
As a promising radar imaging technique, terahertz coded-aperture imaging (TCAI) can achieve high-resolution, forward-looking, and staring imaging by producing spatiotemporal independent signals with coded apertures. In this paper, we propose a three-dimensional (3D) TCAI architecture based on single input multiple output (SIMO) technology, which can reduce the coding and sampling times sharply. The coded aperture applied in the proposed TCAI architecture loads either purposive or random phase modulation factor. In the transmitting process, the purposive phase modulation factor drives the terahertz beam to scan the divided 3D imaging cells. In the receiving process, the random phase modulation factor is adopted to modulate the terahertz wave to be spatiotemporally independent for high resolution. Considering human-scale targets, images of each 3D imaging cell are reconstructed one by one to decompose the global computational complexity, and then are synthesized together to obtain the complete high-resolution image. As for each imaging cell, the multi-resolution imaging method helps to reduce the computational burden on a large-scale reference-signal matrix. The experimental results demonstrate that the proposed architecture can achieve high-resolution imaging with much less time for 3D targets and has great potential in applications such as security screening, nondestructive detection, medical diagnosis, etc. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle IMU-to-Segment Assignment and Orientation Alignment for the Lower Body Using Deep Learning
Sensors 2018, 18(1), 302; https://doi.org/10.3390/s18010302
Received: 17 December 2017 / Revised: 9 January 2018 / Accepted: 10 January 2018 / Published: 19 January 2018
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Abstract
Human body motion analysis based on wearable inertial measurement units (IMUs) receives a lot of attention from both the research community and the and industrial community. This is due to the significant role in, for instance, mobile health systems, sports and human computer
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Human body motion analysis based on wearable inertial measurement units (IMUs) receives a lot of attention from both the research community and the and industrial community. This is due to the significant role in, for instance, mobile health systems, sports and human computer interaction. In sensor based activity recognition, one of the major issues for obtaining reliable results is the sensor placement/assignment on the body. For inertial motion capture (joint kinematics estimation) and analysis, the IMU-to-segment (I2S) assignment and alignment are central issues to obtain biomechanical joint angles. Existing approaches for I2S assignment usually rely on hand crafted features and shallow classification approaches (e.g., support vector machines), with no agreement regarding the most suitable features for the assignment task. Moreover, estimating the complete orientation alignment of an IMU relative to the segment it is attached to using a machine learning approach has not been shown in literature so far. This is likely due to the high amount of training data that have to be recorded to suitably represent possible IMU alignment variations. In this work, we propose online approaches for solving the assignment and alignment tasks for an arbitrary amount of IMUs with respect to a biomechanical lower body model using a deep learning architecture and windows of 128 gyroscope and accelerometer data samples. For this, we combine convolutional neural networks (CNNs) for local filter learning with long-short-term memory (LSTM) recurrent networks as well as generalized recurrent units (GRUs) for learning time dynamic features. The assignment task is casted as a classification problem, while the alignment task is casted as a regression problem. In this framework, we demonstrate the feasibility of augmenting a limited amount of real IMU training data with simulated alignment variations and IMU data for improving the recognition/estimation accuracies. With the proposed approaches and final models we achieved 98.57% average accuracy over all segments for the I2S assignment task (100% when excluding left/right switches) and an average median angle error over all segments and axes of 2.91 ° for the I2S alignment task. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle A Polymer Optical Fiber Temperature Sensor Based on Material Features
Sensors 2018, 18(1), 301; https://doi.org/10.3390/s18010301
Received: 30 November 2017 / Revised: 12 January 2018 / Accepted: 16 January 2018 / Published: 19 January 2018
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Abstract
This paper presents a polymer optical fiber (POF)-based temperature sensor. The operation principle of the sensor is the variation in the POF mechanical properties with the temperature variation. Such mechanical property variation leads to a variation in the POF output power when a
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This paper presents a polymer optical fiber (POF)-based temperature sensor. The operation principle of the sensor is the variation in the POF mechanical properties with the temperature variation. Such mechanical property variation leads to a variation in the POF output power when a constant stress is applied to the fiber due to the stress-optical effect. The fiber mechanical properties are characterized through a dynamic mechanical analysis, and the output power variation with different temperatures is measured. The stress is applied to the fiber by means of a 180° curvature, and supports are positioned on the fiber to inhibit the variation in its curvature with the temperature variation. Results show that the sensor proposed has a sensitivity of 1.04 × 10−3 °C−1, a linearity of 0.994, and a root mean squared error of 1.48 °C, which indicates a relative error of below 2%, which is lower than the ones obtained for intensity-variation-based temperature sensors. Furthermore, the sensor is able to operate at temperatures up to 110 °C, which is higher than the ones obtained for similar POF sensors in the literature. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle A Comprehensive Study of a Micro-Channel Heat Sink Using Integrated Thin-Film Temperature Sensors
Sensors 2018, 18(1), 299; https://doi.org/10.3390/s18010299
Received: 16 December 2017 / Revised: 11 January 2018 / Accepted: 16 January 2018 / Published: 19 January 2018
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Abstract
A micro-channel heat sink is a promising cooling method for high power integrated circuits (IC). However, the understanding of such a micro-channel device is not sufficient, because the tools for studying it are very limited. The details inside the micro-channels are not readily
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A micro-channel heat sink is a promising cooling method for high power integrated circuits (IC). However, the understanding of such a micro-channel device is not sufficient, because the tools for studying it are very limited. The details inside the micro-channels are not readily available. In this letter, a micro-channel heat sink is comprehensively studied using the integrated temperature sensors. The highly sensitive thin film temperature sensors can accurately monitor the temperature change in the micro-channel in real time. The outstanding heat dissipation performance of the micro-channel heat sink is proven in terms of maximum temperature, cooling speed and heat resistance. The temperature profile along the micro-channel is extracted, and even small temperature perturbations can be detected. The heat source formed temperature peak shifts towards the flow direction with the increasing flow rate. However, the temperature non-uniformity is independent of flow rate, but solely dependent on the heating power. Specific designs for minimizing the temperature non-uniformity are necessary. In addition, the experimental results from the integrated temperature sensors match the simulation results well. This can be used to directly verify the modeling results, helping to build a convincing simulation model. The integrated sensor could be a powerful tool for studying the micro-channel based heat sink. Full article
(This article belongs to the Special Issue Integrated Sensors)
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Open AccessArticle Bridge Structure Deformation Prediction Based on GNSS Data Using Kalman-ARIMA-GARCH Model
Sensors 2018, 18(1), 298; https://doi.org/10.3390/s18010298
Received: 3 November 2017 / Revised: 17 December 2017 / Accepted: 15 January 2018 / Published: 19 January 2018
Cited by 2 | Viewed by 1277 | PDF Full-text (4195 KB) | HTML Full-text | XML Full-text
Abstract
Bridges are an essential part of the ground transportation system. Health monitoring is fundamentally important for the safety and service life of bridges. A large amount of structural information is obtained from various sensors using sensing technology, and the data processing has become
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Bridges are an essential part of the ground transportation system. Health monitoring is fundamentally important for the safety and service life of bridges. A large amount of structural information is obtained from various sensors using sensing technology, and the data processing has become a challenging issue. To improve the prediction accuracy of bridge structure deformation based on data mining and to accurately evaluate the time-varying characteristics of bridge structure performance evolution, this paper proposes a new method for bridge structure deformation prediction, which integrates the Kalman filter, autoregressive integrated moving average model (ARIMA), and generalized autoregressive conditional heteroskedasticity (GARCH). Firstly, the raw deformation data is directly pre-processed using the Kalman filter to reduce the noise. After that, the linear recursive ARIMA model is established to analyze and predict the structure deformation. Finally, the nonlinear recursive GARCH model is introduced to further improve the accuracy of the prediction. Simulation results based on measured sensor data from the Global Navigation Satellite System (GNSS) deformation monitoring system demonstrated that: (1) the Kalman filter is capable of denoising the bridge deformation monitoring data; (2) the prediction accuracy of the proposed Kalman-ARIMA-GARCH model is satisfactory, where the mean absolute error increases only from 3.402 mm to 5.847 mm with the increment of the prediction step; and (3) in comparision to the Kalman-ARIMA model, the Kalman-ARIMA-GARCH model results in superior prediction accuracy as it includes partial nonlinear characteristics (heteroscedasticity); the mean absolute error of five-step prediction using the proposed model is improved by 10.12%. This paper provides a new way for structural behavior prediction based on data processing, which can lay a foundation for the early warning of bridge health monitoring system based on sensor data using sensing technology. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle A Novel Walking Detection and Step Counting Algorithm Using Unconstrained Smartphones
Sensors 2018, 18(1), 297; https://doi.org/10.3390/s18010297
Received: 13 December 2017 / Revised: 15 January 2018 / Accepted: 17 January 2018 / Published: 19 January 2018
Cited by 5 | Viewed by 1061 | PDF Full-text (1350 KB) | HTML Full-text | XML Full-text
Abstract
Recently, with the development of artificial intelligence technologies and the popularity of mobile devices, walking detection and step counting have gained much attention since they play an important role in the fields of equipment positioning, saving energy, behavior recognition, etc. In this paper,
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Recently, with the development of artificial intelligence technologies and the popularity of mobile devices, walking detection and step counting have gained much attention since they play an important role in the fields of equipment positioning, saving energy, behavior recognition, etc. In this paper, a novel algorithm is proposed to simultaneously detect walking motion and count steps through unconstrained smartphones in the sense that the smartphone placement is not only arbitrary but also alterable. On account of the periodicity of the walking motion and sensitivity of gyroscopes, the proposed algorithm extracts the frequency domain features from three-dimensional (3D) angular velocities of a smartphone through FFT (fast Fourier transform) and identifies whether its holder is walking or not irrespective of its placement. Furthermore, the corresponding step frequency is recursively updated to evaluate the step count in real time. Extensive experiments are conducted by involving eight subjects and different walking scenarios in a realistic environment. It is shown that the proposed method achieves the precision of 93.76 % and recall of 93.65 % for walking detection, and its overall performance is significantly better than other well-known methods. Moreover, the accuracy of step counting by the proposed method is 95.74 % , and is better than both of the several well-known counterparts and commercial products. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle A GPS Phase-Locked Loop Performance Metric Based on the Phase Discriminator Output
Sensors 2018, 18(1), 296; https://doi.org/10.3390/s18010296
Received: 14 December 2017 / Revised: 13 January 2018 / Accepted: 15 January 2018 / Published: 19 January 2018
Cited by 2 | Viewed by 953 | PDF Full-text (1784 KB) | HTML Full-text | XML Full-text
Abstract
We propose a novel GPS phase-lock loop (PLL) performance metric based on the standard deviation of tracking error (defined as the discriminator’s estimate of the true phase error), and explain its advantages over the popular phase jitter metric using theory, numerical simulation, and
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We propose a novel GPS phase-lock loop (PLL) performance metric based on the standard deviation of tracking error (defined as the discriminator’s estimate of the true phase error), and explain its advantages over the popular phase jitter metric using theory, numerical simulation, and experimental results. We derive an augmented GPS phase-lock loop (PLL) linear model, which includes the effect of coherent averaging, to be used in conjunction with this proposed metric. The augmented linear model allows more accurate calculation of tracking error standard deviation in the presence of additive white Gaussian noise (AWGN) as compared to traditional linear models. The standard deviation of tracking error, with a threshold corresponding to half of the arctangent discriminator pull-in region, is shown to be a more reliable/robust measure of PLL performance under interference conditions than the phase jitter metric. In addition, the augmented linear model is shown to be valid up until this threshold, which facilitates efficient performance prediction, so that time-consuming direct simulations and costly experimental testing can be reserved for PLL designs that are much more likely to be successful. The effect of varying receiver reference oscillator quality on the tracking error metric is also considered. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle Motorcycles that See: Multifocal Stereo Vision Sensor for Advanced Safety Systems in Tilting Vehicles
Sensors 2018, 18(1), 295; https://doi.org/10.3390/s18010295
Received: 31 October 2017 / Revised: 17 January 2018 / Accepted: 17 January 2018 / Published: 19 January 2018
Cited by 1 | Viewed by 1725 | PDF Full-text (16648 KB) | HTML Full-text | XML Full-text
Abstract
Advanced driver assistance systems, ADAS, have shown the possibility to anticipate crash accidents and effectively assist road users in critical traffic situations. This is not the case for motorcyclists, in fact ADAS for motorcycles are still barely developed. Our aim was to study
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Advanced driver assistance systems, ADAS, have shown the possibility to anticipate crash accidents and effectively assist road users in critical traffic situations. This is not the case for motorcyclists, in fact ADAS for motorcycles are still barely developed. Our aim was to study a camera-based sensor for the application of preventive safety in tilting vehicles. We identified two road conflict situations for which automotive remote sensors installed in a tilting vehicle are likely to fail in the identification of critical obstacles. Accordingly, we set two experiments conducted in real traffic conditions to test our stereo vision sensor. Our promising results support the application of this type of sensors for advanced motorcycle safety applications. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle Game-Theoretical Design of an Adaptive Distributed Dissemination Protocol for VANETs
Sensors 2018, 18(1), 294; https://doi.org/10.3390/s18010294
Received: 8 November 2017 / Revised: 16 January 2018 / Accepted: 16 January 2018 / Published: 19 January 2018
Cited by 1 | Viewed by 1003 | PDF Full-text (11987 KB) | HTML Full-text | XML Full-text
Abstract
Road safety applications envisaged for Vehicular Ad Hoc Networks (VANETs) depend largely on the dissemination of warning messages to deliver information to concerned vehicles. The intended applications, as well as some inherent VANET characteristics, make data dissemination an essential service and a challenging
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Road safety applications envisaged for Vehicular Ad Hoc Networks (VANETs) depend largely on the dissemination of warning messages to deliver information to concerned vehicles. The intended applications, as well as some inherent VANET characteristics, make data dissemination an essential service and a challenging task in this kind of networks. This work lays out a decentralized stochastic solution for the data dissemination problem through two game-theoretical mechanisms. Given the non-stationarity induced by a highly dynamic topology, diverse network densities, and intermittent connectivity, a solution for the formulated game requires an adaptive procedure able to exploit the environment changes. Extensive simulations reveal that our proposal excels in terms of number of transmissions, lower end-to-end delay and reduced overhead while maintaining high delivery ratio, compared to other proposals. Full article
(This article belongs to the Special Issue Smart Vehicular Mobile Sensing)
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Open AccessArticle Transparent Fingerprint Sensor System for Large Flat Panel Display
Sensors 2018, 18(1), 293; https://doi.org/10.3390/s18010293
Received: 27 November 2017 / Revised: 15 January 2018 / Accepted: 16 January 2018 / Published: 19 January 2018
Cited by 1 | Viewed by 1530 | PDF Full-text (11117 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, we introduce a transparent fingerprint sensing system using a thin film transistor (TFT) sensor panel, based on a self-capacitive sensing scheme. An armorphousindium gallium zinc oxide (a-IGZO) TFT sensor array and associated custom Read-Out IC (ROIC) are implemented for the
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In this paper, we introduce a transparent fingerprint sensing system using a thin film transistor (TFT) sensor panel, based on a self-capacitive sensing scheme. An armorphousindium gallium zinc oxide (a-IGZO) TFT sensor array and associated custom Read-Out IC (ROIC) are implemented for the system. The sensor panel has a 200 × 200 pixel array and each pixel size is as small as 50 μm × 50 μm. The ROIC uses only eight analog front-end (AFE) amplifier stages along with a successive approximation analog-to-digital converter (SAR ADC). To get the fingerprint image data from the sensor array, the ROIC senses a capacitance, which is formed by a cover glass material between a human finger and an electrode of each pixel of the sensor array. Three methods are reviewed for estimating the self-capacitance. The measurement result demonstrates that the transparent fingerprint sensor system has an ability to differentiate a human finger’s ridges and valleys through the fingerprint sensor array. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Multi-Sensor Data Integration Using Deep Learning for Characterization of Defects in Steel Elements
Sensors 2018, 18(1), 292; https://doi.org/10.3390/s18010292
Received: 9 December 2017 / Revised: 16 January 2018 / Accepted: 16 January 2018 / Published: 19 January 2018
Cited by 4 | Viewed by 1443 | PDF Full-text (5634 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Nowadays, there is a strong demand for inspection systems integrating both high sensitivity under various testing conditions and advanced processing allowing automatic identification of the examined object state and detection of threats. This paper presents the possibility of utilization of a magnetic multi-sensor
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Nowadays, there is a strong demand for inspection systems integrating both high sensitivity under various testing conditions and advanced processing allowing automatic identification of the examined object state and detection of threats. This paper presents the possibility of utilization of a magnetic multi-sensor matrix transducer for characterization of defected areas in steel elements and a deep learning based algorithm for integration of data and final identification of the object state. The transducer allows sensing of a magnetic vector in a single location in different directions. Thus, it enables detecting and characterizing any material changes that affect magnetic properties regardless of their orientation in reference to the scanning direction. To assess the general application capability of the system, steel elements with rectangular-shaped artificial defects were used. First, a database was constructed considering numerical and measurements results. A finite element method was used to run a simulation process and provide transducer signal patterns for different defect arrangements. Next, the algorithm integrating responses of the transducer collected in a single position was applied, and a convolutional neural network was used for implementation of the material state evaluation model. Then, validation of the obtained model was carried out. In this paper, the procedure for updating the evaluated local state, referring to the neighboring area results, is presented. Finally, the results and future perspective are discussed. Full article
(This article belongs to the Special Issue Small Devices and the High-Tech Society)
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Open AccessArticle Joint Bearing and Range Estimation of Multiple Objects from Time-Frequency Analysis
Sensors 2018, 18(1), 291; https://doi.org/10.3390/s18010291
Received: 5 November 2017 / Revised: 22 December 2017 / Accepted: 15 January 2018 / Published: 19 January 2018
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Abstract
Direction-of-arrival (DOA) and range estimation is an important issue of sonar signal processing. In this paper, a novel approach using Hilbert-Huang transform (HHT) is proposed for joint bearing and range estimation of multiple targets based on a uniform linear array (ULA) of hydrophones.
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Direction-of-arrival (DOA) and range estimation is an important issue of sonar signal processing. In this paper, a novel approach using Hilbert-Huang transform (HHT) is proposed for joint bearing and range estimation of multiple targets based on a uniform linear array (ULA) of hydrophones. The structure of this ULA based on micro-electro-mechanical systems (MEMS) technology, and thus has attractive features of small size, high sensitivity and low cost, and is suitable for Autonomous Underwater Vehicle (AUV) operations. This proposed target localization method has the following advantages: only a single snapshot of data is needed and real-time processing is feasible. The proposed algorithm transforms a very complicated nonlinear estimation problem to a simple nearly linear one via time-frequency distribution (TFD) theory and is verified with HHT. Theoretical discussions of resolution issue are also provided to facilitate the design of a MEMS sensor with high sensitivity. Simulation results are shown to verify the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Smart Sensors for Mechatronic and Robotic Systems)
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Open AccessArticle Wide-Field Fluorescence Microscopy of Real-Time Bioconjugation Sensing
Sensors 2018, 18(1), 290; https://doi.org/10.3390/s18010290
Received: 6 December 2017 / Revised: 12 January 2018 / Accepted: 16 January 2018 / Published: 19 January 2018
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Abstract
We apply wide-field fluorescence microscopy to measure real-time attachment of photosynthetic proteins to plasmonically active silver nanowires. The observation of this effect is enabled, on the one hand, by sensitive detection of fluorescence and, on the other hand, by plasmonic enhancement of protein
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We apply wide-field fluorescence microscopy to measure real-time attachment of photosynthetic proteins to plasmonically active silver nanowires. The observation of this effect is enabled, on the one hand, by sensitive detection of fluorescence and, on the other hand, by plasmonic enhancement of protein fluorescence. We examined two sample configurations with substrates being a bare glass coverslip and a coverslip functionalized with a monolayer of streptavidin. The different preparation of the substrate changes the observed behavior as far as attachment of the protein is concerned as well as its subsequent photobleaching. For the latter substrate the conjugation process is measurably slower. The described method can be universally applied in studying protein-nanostructure interactions for real-time fluorescence-based sensing. Full article
(This article belongs to the Special Issue Novel Approaches to Biosensing with Nanoparticles)
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Open AccessArticle Mechanical Structural Design of a MEMS-Based Piezoresistive Accelerometer for Head Injuries Monitoring: A Computational Analysis by Increments of the Sensor Mass Moment of Inertia
Sensors 2018, 18(1), 289; https://doi.org/10.3390/s18010289
Received: 18 December 2017 / Revised: 17 January 2018 / Accepted: 18 January 2018 / Published: 19 January 2018
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Abstract
This work focuses on the proof-mass mechanical structural design improvement of a tri-axial piezoresistive accelerometer specifically designed for head injuries monitoring where medium-G impacts are common; for example, in sports such as racing cars or American Football. The device requires the highest sensitivity
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This work focuses on the proof-mass mechanical structural design improvement of a tri-axial piezoresistive accelerometer specifically designed for head injuries monitoring where medium-G impacts are common; for example, in sports such as racing cars or American Football. The device requires the highest sensitivity achievable with a single proof-mass approach, and a very low error (<1%) as the accuracy for these types of applications is paramount. The optimization method differs from previous work as it is based on the progressive increment of the sensor proof-mass mass moment of inertia (MMI) in all three axes. Three different designs are presented in this study, where at each step of design evolution, the MMI of the sensor proof-mass gradually increases in all axes. The work numerically demonstrates that an increment of MMI determines an increment of device sensitivity with a simultaneous reduction of cross-axis sensitivity in the particular axis under study. This is due to the linkage between the external applied stress and the distribution of mass (of the proof-mass), and therefore of its mass moment of inertia. Progressively concentrating the mass on the axes where the piezoresistors are located (i.e., x- and y-axis) by increasing the MMI in the x- and y-axis, will undoubtedly increase the longitudinal stresses applied in that areas for a given external acceleration, therefore increasing the piezoresistors fractional resistance change and eventually positively affecting the sensor sensitivity. The final device shows a sensitivity increase of about 80% in the z-axis and a reduction of cross-axis sensitivity of 18% respect to state-of-art sensors available in the literature from a previous work of the authors. Sensor design, modelling, and optimization are presented, concluding the work with results, discussion, and conclusion. Full article
(This article belongs to the Special Issue I3S 2017 Selected Papers)
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